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Reading/ writing file and pandas
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‎class.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "32dcc4c5",
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"metadata": {},
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"source": [
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"Class"
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]
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},
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{
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"cell_type": "markdown",
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"id": "48b72d0b",
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"metadata": {},
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"source": [
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"A class is like an object constructor or a blueprint for creating objects"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f66cb092",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10\n"
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]
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}
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],
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"source": [
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"class myclass:\n",
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" x =10;\n",
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"p1 =myclass();\n",
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"print(p1.x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "43a52efd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"red\n",
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"31.400000000000002\n"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"class Circle:\n",
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" def __init__(self, r, c):\n",
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" self.radius = r\n",
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" self.color = c\n",
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"\n",
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" def add(self, r):\n",
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" self.radius = r\n",
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" return 2 * 3.14 * r\n",
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"\n",
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" \n",
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"\n",
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"c1 = Circle(10, 'red')\n",
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"print(c1.color)\n",
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"p = c1.add(5)\n",
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"print(p)\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21862d57",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎file.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d224a3d2",
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"metadata": {},
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"source": [
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"##Reading File with Open"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "d8d31f0f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Hello My n\n"
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]
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}
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],
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"source": [
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"with open(\"helllo.txt\",\"r\") as f2: # use of with statement is better practice to open a file becuase it automatically closes the file\n",
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" # filest = f2.read();\n",
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" # print(filest);\n",
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" # print(f2.closed);\n",
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" # print(filest)\n",
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" # line = f2.readline() # read first line \n",
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" # line2 = f2.readline() # read second line it called by sequence\n",
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" # for line in f2: # each line over loop\n",
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" # print(line)\n",
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" specialline = f2.readline(10) # till number of character\n",
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" print(specialline)\n",
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" \n",
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"\n",
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"\n",
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"\n",
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"# f1 = open(\"helllo.txt\",\"r\"); # to normal open the file \n",
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"# ff = f1.read();\n",
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"# print(ff)\n",
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"# print(f1.closed);\n",
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"# print(filest)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "46e419e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"write.txt\",\"w\") as f3: # Created a file or we can write on existing file but it will remove all previous thing on file\n",
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" f3.write(\"AI is dangerous:::::\\n\")\n",
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" f3.write(\"Yellow\\n\")\n",
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"\n",
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"with open(\"write.txt\",\"a\") as f4: # it will not create a new file but add lines into existing file without removing previous text.\n",
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" f4.write(\"this will go to next line \") \n",
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"\n",
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"\n",
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"##Write data of one file into other file \n",
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"with open(\"helllo.txt\",\"r\") as readfile:\n",
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" with open(\"newfile\",\"w\")as writefile:\n",
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" for line in readfile:\n",
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" writefile.write(line)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "92f9de46",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎file1.xlsx

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Binary file not shown.

‎helllo.txt

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AI is dangerous:::::

‎newfile

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AI is dangerous:::::

‎pandas.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4c3a8bad",
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"metadata": {},
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"source": [
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"##Pandas :: Loading"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f83db65",
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"metadata": {},
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"source": [
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"Pandas is a popular library for data analysis.\n",
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"we can import pandas by using import command \n",
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"import pandas --"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "07e0d62b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Album Year\n",
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"0 Believer 2018\n",
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"1 Masti 2020\n",
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"2 Fade way 2009\n",
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" Album\n",
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"0 Believer\n",
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"1 Masti\n",
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"2 Fade way\n",
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"P-00089\n",
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"MARMUL FALCON TRADE & Co\n",
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"Believer\n"
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]
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}
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],
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"source": [
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"import pandas as pd;\n",
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"\n",
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"df = pd.read_excel('file1.xlsx'); # using pandas method read_excel to read excel files\n",
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"x = df[['Customer']] #pick one column \n",
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"\n",
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"# print(df.head());\n",
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"\n",
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"album = {\n",
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" 'Album':['Believer','Masti','Fade way'],\n",
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" 'Year':['2018','2020','2009']\n",
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"\n",
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"}\n",
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"songs_info = pd.DataFrame(album)\n",
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"print(songs_info)\n",
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"y = songs_info[['Album']]; # take one or multiple column to make a different dataframe from an existing dataFrame or file\n",
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"print(y)\n",
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"\n",
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"#iloc[r,c] method to get value from specific row or column r - row number c is column number;\n",
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"print(df.iloc[0,1])\n",
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"print(df.loc[3,'Customer'])\n",
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"songs_infos = songs_info\n",
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"songs_infos.index=[\"a\",\"b\",\"c\"] # changing index of a dataframe \n",
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"print(songs_infos.loc['a','Album'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83438a76",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎write.txt

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AI is dangerous:::::
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Yellow
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this will go to next line

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